We started our analysis by trying to get a basic sense of the properties of the floods recorded in the dataset. This meant looking for things like the spatial and temporal distribution of flood, the distribution of flood size, duration, and area affected, and distributions of . We wanted to see what variation there was between floods, and if we could get any indicators of what was explaining that variation.
The above plots mostly follow a ‘power law’ distribution, with the bulk of the mass residing in the first 20 percent of the distribution and with a thin but long tail of extreme outlying events. Subsetting and faceting the distributions by type, cause, severity didn’t yield significantly different distributions.
This plot shows the spatial distribution of floods by cause. Not surprisingly, the plot indicates that the geography has a significant effect on the type of flooding experinced in different parts of the world. Floods due to Monsoon, for example happen primarily in the Indian Ocean and Southeast Asia; floods cause by snow melt happen at more northerly mountainous regions; floods caused by hurricaines and tropical storms happen mostly along the coast in warmer, more tropical latitudes. Floods caused by heavy rainfall were present all over the world (except in expected locations like desserts). Futher (and also not surprisingly), we noticed that floods occur most frequently near rivers and coastlines.
The density maps allow for somewhat easier interpretation of where the bulk of floods occur. As metioned previously and is vividly indicated by the monsoon density map, densities often center near rivers and coastlines, such as the Gangese River Delta.
This section explores the relationship between Geopotential Heigh and Flood Magnitude by two major datasets. This first one is the “NOAA_Daily_phi_500mb.nc”, which provides the geopotential heigh values and the second one is the “GlobalFloodsRecord.xls”, which provides different kinds of flood data. In addition, we focous on both dataset in 2012 to 2013 as well as 2014 to 2015 within the region of the United States.
scatter plot is utilized to glance at corresponding geopotential heigh values and flood magnitude. Besides, a simple linear regression models is used to determine the relationship between the two variables: \[floodMagnitude = \beta_0 + \beta_1 * geopotentialHeigh\]
All data has been cleaned up and exported as csv file. The first five corresponding geopotential heigh and magnitude values from 2012 to 2013 are displayed below. Since each flood appearen in a period of time, the corresponding geopotential heigh value is taking as the mean during that period of time
## phi_value1213 magnitude1213
## 1 5730.727 5.9
## 2 5824.222 6.3
## 3 5823.667 5.7
## 4 5807.500 6.4
## 5 5807.500 6.1
## 6 5735.500 5.4Below are the data values from 2014 to 2015. Same as above, the corresponding geopotential heigh value is takeing as mean during that period of time
## phi_value1415 magnitude1415
## 1 5759.889 6.6
## 2 5792.154 6.2
## 3 5874.000 5.9
## 4 5870.500 5.4
## 5 5803.000 5.7
## 6 5843.536 8.0
The above graphics display the relationship between geopotential heigh and flood magnitude in an Euclidean space, where the x axis represents geopotential heigh(phi) value and the y axis preresents the flood magnitude value. The size of the bubble also indicate how large the corresponding phi value is.
The correlation between these variables are also included below
geopotential heigh and flood magnitude correlation from 2012-2013:
## [1] 0.3698957geopotential heigh and flood magnitude correlation from 2014-2015:
## [1] 0.4008081finally a simple linear regression model is being appied to the dataset.
geopotential heigh and flood magnitude regression from 2012-2013:
##
## Call:
## lm(formula = magnitude1213 ~ phi_value1213, data = phi_magni1213)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.97609 -0.60638 0.03615 0.38745 1.29840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -13.227083 13.195664 -1.002 0.334
## phi_value1213 0.003278 0.002284 1.435 0.175
##
## Residual standard error: 0.7168 on 13 degrees of freedom
## Multiple R-squared: 0.1368, Adjusted R-squared: 0.07042
## F-statistic: 2.061 on 1 and 13 DF, p-value: 0.1748geopotential heigh and flood magnitude regression from 2014-2015:
##
## Call:
## lm(formula = magnitude1415 ~ phi_value1415, data = phi_magni1415)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4670 -0.5770 0.2512 0.6461 1.8941
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -19.224277 14.232740 -1.351 0.1956
## phi_value1415 0.004335 0.002477 1.750 0.0993 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.141 on 16 degrees of freedom
## Multiple R-squared: 0.1606, Adjusted R-squared: 0.1082
## F-statistic: 3.062 on 1 and 16 DF, p-value: 0.09928visualize the result:
Both the shaped areas represent the 99% confident level in the regression model.
From the above res ult, we can not find a statiscal significant relationship between geopotential heigh and flood magnitude in the Uinted States from 2012 to 2013 and 2014 to 2015 data.
Flood have a huge impact on population and economy. Heavy rain leads to destruction, death, displacement, healness, reparations. Some countries may receive more floods based on its location (for example India) but some countries also don’t have the capacity to prevent, prepare population, manage flood and repare.
In this analysis we will try to analyse what characteristic influence the impact of a flood.
Different region groups have countries of varying income levels.
### Displacement
The scatterplot of people displaced for high income is concentrated in the bottom, which means that High Income countries tend to not displace people much regardless of the magnitude of the flood.
In contrast, low income, lower middle income, and upper middle income countries seem to be more middle-heavy. These countries also seem to displace more people as magnitude increases.
The scatterplot of people dead for high income is very bottom-heavy, even more so than in displacement. As income level goes down, we notice that a lot more points are plotted on top. We can quantify this finding further by running a regression analysis by country income level.
## Call:
## Model: peopleDisplaced ~ magnitude | incomeGroup
## Data: datc
##
## Coefficients:
## (Intercept) magnitude
## Low income -356801.66 81579.940
## Lower middle income -1822435.25 403441.847
## Upper middle income -624934.98 137749.151
## High income: nonOECD -79965.76 17940.510
## High income: OECD -12639.72 4798.086
##
## Degrees of freedom: 4159 total; 4149 residual
## Residual standard error: 1243627## Call:
## Model: peopleDead ~ magnitude | incomeGroup
## Data: datc
##
## Coefficients:
## (Intercept) magnitude
## Low income -121.72851 34.940425
## Lower middle income -642.53399 172.759060
## Upper middle income -102.40638 52.260055
## High income: nonOECD -334.65530 79.528348
## High income: OECD 57.91202 -7.579596
##
## Degrees of freedom: 4159 total; 4149 residual
## Residual standard error: 3654.833There is a trend of the slope coefficient on the variable “magnitude” as income level goes down. This means that the higher the magnitude, the higher number of people are displaced as country income level goes down. Intuitively, this can be interepreted as since high-income countries are better prepared for floods in all magnitude, they tend to displace only few people regardless of magnitude. However, countries with low income do not have resources to prepare for floods with large magnitude. Therefore, they tend to displace more people as magnitude intensifies.
Similarly, the coefficient on magnitude to predict number of death is not even positive for high income OECD countries. However, as the income level goes down, the importance of magnitude in predicting the number of deaths increases. The reasoning is the same as displacement. Countries with high income are better prepared, and they take good measures to prevent deaths from happeneing, even in cases of severe magnitudes. Countries with lower incomes do not have such resources.
Now, let’s look at differing effects of affected area by country income group.
Similar to magnitude, in High Income OECD countreis, the scatterplot is bottom-heavy. Regardless of the size of the affected area, very few people were displaced. However for Lower middle income and upper middle income countries, the scatterplot is top-heavy, which means that the larger the affected area, the more people were displaced.
The correlation between number of people dead and the area of affected region seems to be lower for deaths. Again, for High-OECD countries, the scatterplot is very much bottom-heavy.
## Call:
## Model: peopleDisplaced ~ affectedSqKm | incomeGroup
## Data: datc
##
## Coefficients:
## (Intercept) affectedSqKm
## Low income 42097.31 0.39752791
## Lower middle income 157822.82 1.81979411
## Upper middle income 66786.57 0.43434747
## High income: nonOECD 12158.40 0.04800677
## High income: OECD 11032.64 0.01281745
##
## Degrees of freedom: 4159 total; 4149 residual
## Residual standard error: 1253774## Call:
## Model: peopleDead ~ affectedSqKm | incomeGroup
## Data: datc
##
## Coefficients:
## (Intercept) affectedSqKm
## Low income 53.67329 1.252309e-04
## Lower middle income 257.64169 1.865733e-04
## Upper middle income 184.20074 -1.624620e-05
## High income: nonOECD 84.41207 1.539756e-04
## High income: OECD 21.09149 -2.687088e-05
##
## Degrees of freedom: 4159 total; 4149 residual
## Residual standard error: 3656.426The regression analysis indicates that similar trend of strong correlation beween area affected and number of people displaced as the country income level goes down.
The relationship between the size of area affected and number of deaths seems to be much weaker. Interestingly, high income OECD countries and upper middle income countries have a negative relationship between affected square kilometers and number of deaths. While the coefficient is not very large, this can mean that when there are floods that affect large areas, countries predict it and take measures to prevent deaths.
More detailed data can be found when countries are analyzed at a subregion level.
Size of Affected Area
Subregions that displaced more people as the size of affected region increased were South America, Eastern Asia, and Southern Asia. Subregions that displaced very few people regardless of the size of the affected area were western Asia and Austria and New Zealand.
Subregions that experienced more deaths as the size of affected region increased were Southern Asia and Eastern Asia Subregions that experienced few deaths regardless of the size of the affected area were Western Asia and Austria/New Zealand.
While Landslides and Avalanches seem to cause floods only between magnitudes of 4 and 5, Heavy rain seems to cause floods of varying degrees of magnitudes.
Snow/Ice Melt and Tsunami can affect a wide array of sizes of lands. Landslides/Avalanches seem to usually affect small areas, and Monsoons seem to affect large areas.
Monsoons and Hurricanes/Tropical Storm seem to displace many people while Landslides/avalanches and snow/ice melt seem to usually displace fewer people. Tsunamis always seem to kill many people, while snow/ice melt seems to always kill only few people.
Northern America and Northern Europe seem to have very high starting point of damage in USD probably because since the GDP is high, when damage is done, it is more expensive to recover from the damage. Middle Africa seems to have the lowest starting point in terms of damage in USD.
South-Eastern Asia, followed by Southern Asia and then Eastern Asia and Northen America, experience the most number of floods. For each region, over 50% or more floods are caused by heavy rain. In the case of Southern Asia, a large portion of floods are caused by Monsoon. Hurricane/Tropical Storms seem to happen quite often in South-Eastern Asia and Eastern Asia.
Let’s first take a look at the evolution of flood in time
We note that there is an augmentation of the severity of flood in the last decade. But there is no specific cause that can explain it.
When we analyze the repartion of floods by region, we note that there are some region more affected and that some region are more targeted by specific type of disaster. For example:
Based on these 2 graphs, we note 2 things:
In what type of countries does flood have a greater impact on population? To answer this question we will gather and merge data about Human development index (HDI), life expectancy, expected number of school year, Gross National Income (GNI) per capita.
When we look at people displaced, we see that it mainly affect Asian and African countries and mainly countries with low (below 0.6) or medium (between 0.6 and 0.75) HDI.
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -582.5722965 686.734554 -0.8483224 0.39630772
## HDI -1532.8972850 777.478162 -1.9716274 0.04871894
## lifeExp 25.8339559 15.253336 1.6936594 0.09040560
## GNIPerCapita -0.7485036 1.219345 -0.6138573 0.53934354The regression analysis confirm that country characteristics does not impact the number of dead people per flood.
##
## Call:
## lm(formula = peopleDisplaced ~ HDI + lifeExp + GNIPerCapita,
## data = flood2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -607416 -184480 -116856 -3324 39738981
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -688620.7 263012.8 -2.618 0.00887 **
## HDI -2024673.1 297766.7 -6.800 1.20e-11 ***
## lifeExp 30766.7 5841.9 5.267 1.46e-07 ***
## GNIPerCapita 808.3 467.0 1.731 0.08354 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1272000 on 4121 degrees of freedom
## Multiple R-squared: 0.01174, Adjusted R-squared: 0.01102
## F-statistic: 16.31 on 3 and 4121 DF, p-value: 1.541e-10There is a true correlation between the stage of development of a country and the number of displaced people during floods.
When it comes to death toll, no country is protected against a huge event as a huge tsunami or a hurricane. But facing heavy rain, developed country have better infrastructure and a greater capacity to take care of the people touched by such event. They also have the ability to quickly repair in order to make the population suffer a minimum time so that they don’t have to move.